OpenRaiser/Intern-Atlas huggingface dataset:
Critique of Impure Reason: Unveiling the reasoning behaviour of medical Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

Background: Despite the current ubiquity of Large Language Models (LLMs) across the medical domain, there is a surprising lack of studies which address their reasoning behaviour. We emphasise the importance of understanding reasoning behaviour as opposed to high-level prediction accuracies, since it is equivalent to explainable AI (XAI) in this context. In particular, achieving XAI in medical LLMs used in the clinical domain will have a significant impact across the healthcare sector. Results: Therefore, in this work, we adapt the existing concept of reasoning behaviour and articulate its interpretation within the specific context of medical LLMs. We survey and categorise current state-of-the-art approaches for modeling and evaluating reasoning reasoning in medical LLMs. Additionally, we propose theoretical frameworks which can empower medical professionals or machine learning engineers to gain insight into the low-level reasoning operations of these previously obscure models. We also outline key open challenges facing the development of Large Reasoning Models. Conclusion: The subsequent increased transparency and trust in medical machine learning models by clinicians as well as patients will accelerate the integration, application as well as further development of medical AI for the healthcare system as a whole.
Combinatorial Optimization with Quantum Computers
Venue: arXiv cs.ET 2025
Year: 2025

Quantum computers leverage the principles of quantum mechanics to do computation with a potential advantage over classical computers. While a single classical computer transforms one particular binary input into an output after applying one operator to the input, a quantum computer can apply the operator to a superposition of binary strings to provide a superposition of binary outputs, doing computation apparently in parallel. This feature allows quantum computers to speed up the computation compared to classical algorithms. Unsurprisingly, quantum algorithms have been proposed to solve optimization problems in quantum computers. Furthermore, a family of quantum machines called quantum annealers are specially designed to solve optimization problems. In this paper, we provide an introduction to quantum optimization from a practical point of view. We introduce the reader to the use of quantum annealers and quantum gate-based machines to solve optimization problems.
Bi-directional Mapping of Morphology Metrics and 3D City Blocks for Enhanced Characterization and Generation of Urban Form
Venue: arXiv cs.CE 2025
Year: 2025

Urban morphology, examining city spatial configurations, links urban design to sustainability. Morphology metrics play a fundamental role in performance-driven computational urban design (CUD) which integrates urban form generation, performance evaluation and optimization. However, a critical gap remains between performance evaluation and complex urban form generation, caused by the disconnection between morphology metrics and urban form, particularly in metric-to-form workflows. It prevents the application of optimized metrics to generate improved urban form with enhanced urban performance. Formulating morphology metrics that not only effectively characterize complex urban forms but also enable the reconstruction of diverse forms is of significant importance. This paper highlights the importance of establishing a bi-directional mapping between morphology metrics and complex urban form to enable the integration of urban form generation with performance evaluation. We present an approach that can 1) formulate morphology metrics to both characterize urban forms and in reverse, retrieve diverse similar 3D urban forms, and 2) evaluate the effectiveness of morphology metrics in representing 3D urban form characteristics of blocks by comparison. We demonstrate the methodology with 3D urban models of New York City, covering 14,248 blocks. We use neural networks and information retrieval for morphology metric encoding, urban form clustering and morphology metric evaluation. We identified an effective set of morphology metrics for characterizing block-scale urban forms through comparison. The proposed methodology tightly couples complex urban forms with morphology metrics, hence it can enable a seamless and bidirectional relationship between urban form generation and optimization in performance-driven urban design towards sustainable urban design and planning.
MAD-NG, a standalone multiplatform tool for linear and non-linear optics design and optimisation
Venue: arXiv cs.CE 2025
Year: 2025

The paper will provide an overview of the capabilities of the Methodical Accelerator Design Next Generation (MAD-NG) tool. MAD-NG is a standalone, all-in-one, multi-platform tool well-suited for linear and nonlinear optics design and optimization, and has already been used in large-scale studies such as HiLumi-LHC or FCC-ee. It embeds LuaJIT, an extremely fast tracing just-in-time compiler for the Lua programming language, delivering exceptional versatility and performance for the forefront of computational physics. The core of MAD-NG relies on the fast Generalized Truncated Power Series Algebra (GTPSA) library, which has been specially developed to handle many parameters and high-order differential algebra, including Lie map operators. This ecosystem offers powerful features for the analysis and optimization of linear and nonlinear optics, thanks to the fast parametric nonlinear normal forms and the polyvalent matching command. A few examples and results will complete this overview of the MAD-NG application.
SafeCFG: Controlling Harmful Features with Dynamic Safe Guidance for Safe Generation
Venue: arXiv cs.CV 2025
Year: 2025

Diffusion models (DMs) have demonstrated exceptional performance in text-to-image tasks, leading to their widespread use. With the introduction of classifier-free guidance (CFG), the quality of images generated by DMs is significantly improved. However, one can use DMs to generate more harmful images by maliciously guiding the image generation process through CFG. Existing safe alignment methods aim to mitigate the risk of generating harmful images but often reduce the quality of clean image generation. To address this issue, we propose SafeCFG to adaptively control harmful features with dynamic safe guidance by modulating the CFG generation process. It dynamically guides the CFG generation process based on the harmfulness of the prompts, inducing significant deviations only in harmful CFG generations, achieving high quality and safety generation. SafeCFG can simultaneously modulate different harmful CFG generation processes, so it could eliminate harmful elements while preserving high-quality generation. Additionally, SafeCFG provides the ability to detect image harmfulness, allowing unsupervised safe alignment on DMs without pre-defined clean or harmful labels. Experimental results show that images generated by SafeCFG achieve both high quality and safety, and safe DMs trained in our unsupervised manner also exhibit good safety performance.
Adaptable TeaStore
Venue: arXiv cs.DC 2025
Year: 2025

Modern cloud-native systems require adapting dynamically to changing operational conditions, including service outages, traffic surges, and evolving user requirements. While existing benchmarks provide valuable testbeds for performance and scalability evaluation, they lack explicit support for studying adaptation mechanisms, reconfiguration strategies, and graceful degradation. These limitations hinder systematic research on self-adaptive architectures in realistic cloud environments. To cover this gap, we introduce Adaptable TeaStore, an extension of the renowned TeaStore architecture that incorporates adaptability as a first-class design concern. Our extension distinguishes between mandatory and optional services, supports multiple component versions -- with varying resource requirements and functionality levels -- considers the outsourcing of functionalities to external providers, and provides local cache mechanisms for performance and resilience. These features enable the systematic exploration of reconfiguration policies across diverse operational scenarios. We discuss a broad catalogue of reference adaptation scenarios centred around Adaptable TeaStore, useful to evaluate the ability of a given adaptation technology to address conditions such as component unavailability, cyberattacks, provider outages, benign/malicious traffic increases, and user-triggered reconfigurations. Moreover, we present an open-source implementation of the architecture with APIs for metrics collection and adaptation triggers, to enable reproducible experiments.
Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis
Venue: arXiv cs.LG 2025
Year: 2025

The increasing demand for privacy-preserving data analytics in various domains necessitates solutions for synthetic data generation that rigorously uphold privacy standards. We introduce the DP-FedTabDiff framework, a novel integration of Differential Privacy, Federated Learning and Denoising Diffusion Probabilistic Models designed to generate high-fidelity synthetic tabular data. This framework ensures compliance with privacy regulations while maintaining data utility. We demonstrate the effectiveness of DP-FedTabDiff on multiple real-world mixed-type tabular datasets, achieving significant improvements in privacy guarantees without compromising data quality. Our empirical evaluations reveal the optimal trade-offs between privacy budgets, client configurations, and federated optimization strategies. The results affirm the potential of DP-FedTabDiff to enable secure data sharing and analytics in highly regulated domains, paving the way for further advances in federated learning and privacy-preserving data synthesis.
Towards Interpretable Radiology Report Generation via Concept Bottlenecks using a Multi-Agentic RAG
Venue: arXiv cs.IR 2025
Year: 2025

Deep learning has advanced medical image classification, but interpretability challenges hinder its clinical adoption. This study enhances interpretability in Chest X-ray (CXR) classification by using concept bottleneck models (CBMs) and a multi-agent Retrieval-Augmented Generation (RAG) system for report generation. By modeling relationships between visual features and clinical concepts, we create interpretable concept vectors that guide a multi-agent RAG system to generate radiology reports, enhancing clinical relevance, explainability, and transparency. Evaluation of the generated reports using an LLM-as-a-judge confirmed the interpretability and clinical utility of our model's outputs. On the COVID-QU dataset, our model achieved 81% classification accuracy and demonstrated robust report generation performance, with five key metrics ranging between 84% and 90%. This interpretable multi-agent framework bridges the gap between high-performance AI and the explainability required for reliable AI-driven CXR analysis in clinical settings. Our code is available at https://github.com/tifat58/IRR-with-CBM-RAG.git.
Algebraic Evaluation Theorems
Venue: arXiv cs.AI 2025
Year: 2025

Majority voting (MV) is the prototypical ``wisdom of the crowd'' algorithm. Theorems considering when MV is optimal for group decisions date back to Condorcet's 1785 jury \emph{decision} theorem. The same error independence assumption underlying the theorem can be used to prove a jury \emph{evaluation} theorem that does purely algebraic evaluation (AE) of juror performance based on a batch of their decisions. Three or more binary jurors are enough to obtain the only two possible statistics of their correctness on a test they took. AE is superior to MV in three ways. First, its empirical assumptions are looser and can handle jurors less than 50\% accurate in making decisions. Second, it has point-like precision in evaluating them given its assumption of error independence. This precision enables a multi-accuracy approach that has higher labeling accuracy than MV and comes with empirical uncertainty bounds. And, third, it is self-alarming about the failure of its error independence assumption. Experiments using demographic data from the American Community Survey confirm the practical utility of AE over MV. Two implications of the theorem for AI safety are discussed - a principled way to terminate infinite monitoring chains (who grades the graders?) and the super-alignment problem (how do we evaluate agents doing tasks we do not understand?).
The impact of behavioral diversity in multi-agent reinforcement learning
Venue: arXiv cs.AI 2025
Year: 2025

Many of the world's most pressing issues, such as climate change and global peace, require complex collective problem-solving skills. Recent studies indicate that diversity in individuals' behaviors is key to developing such skills and increasing collective performance. Yet behavioral diversity in collective artificial learning is understudied, with today's machine learning paradigms commonly favoring homogeneous agent strategies over heterogeneous ones, mainly due to computational considerations. In this work, we employ diversity measurement and control paradigms to study the impact of behavioral heterogeneity in several facets of multi-agent reinforcement learning. Through experiments in team play and other cooperative tasks, we show the emergence of unbiased behavioral roles that improve team outcomes; how behavioral diversity synergizes with morphological diversity; how diverse agents are more effective at finding cooperative solutions in sparse reward settings; and how behaviorally heterogeneous teams learn and retain latent skills to overcome repeated disruptions. Overall, our results indicate that, by controlling diversity, we can obtain non-trivial benefits over homogeneous training paradigms, demonstrating that diversity is a fundamental component of collective artificial learning, an insight thus far overlooked.
XR for All: Understanding Developers' Perspectives on Accessibility Integration in Extended Reality
Venue: arXiv cs.HC 2025
Year: 2025

As immersive technologies enable unique, multimodal interaction methods, developers must also use tailored methods to support user accessibility, distinct from traditional software practices. We interviewed 25 industry extended reality (XR) developers, including freelancers, startups, midsize, and big tech companies about their motivations, techniques, barriers, and attitudes towards incorporating accessibility features in their XR apps. Our study revealed a variety of challenges, including conflicting priorities between application and platform developers regarding accessibility infrastructure; rapid development culture hindering accessible development; and the lack of accessible interaction design considerations at the ideation, design, and early prototyping stages. As a comprehensive set of XR accessibility guidelines has yet to be established, we also compiled and evaluated a set of accessibility guidelines for 3D virtual worlds and addressed their limitations when applied to XR. Finally, we inform the creation of effective support methods for industry developers.
SOUS VIDE: Cooking Visual Drone Navigation Policies in a Gaussian Splatting Vacuum
Venue: arXiv cs.RO 2025
Year: 2025

We propose a new simulator, training approach, and policy architecture, collectively called SOUS VIDE, for end-to-end visual drone navigation. Our trained policies exhibit zero-shot sim-to-real transfer with robust real-world performance using only onboard perception and computation. Our simulator, called FiGS, couples a computationally simple drone dynamics model with a high visual fidelity Gaussian Splatting scene reconstruction. FiGS can quickly simulate drone flights producing photorealistic images at up to 130 fps. We use FiGS to collect 100k-300k image/state-action pairs from an expert MPC with privileged state and dynamics information, randomized over dynamics parameters and spatial disturbances. We then distill this expert MPC into an end-to-end visuomotor policy with a lightweight neural architecture, called SV-Net. SV-Net processes color image, optical flow and IMU data streams into low-level thrust and body rate commands at 20 Hz onboard a drone. Crucially, SV-Net includes a learned module for low-level control that adapts at runtime to variations in drone dynamics. In a campaign of 105 hardware experiments, we show SOUS VIDE policies to be robust to 30% mass variations, 40 m/s wind gusts, 60% changes in ambient brightness, shifting or removing objects from the scene, and people moving aggressively through the drone's visual field. Code, data, and experiment videos can be found on our project page: https://stanfordmsl.github.io/SousVide/.
CensorLab: A Testbed for Censorship Experimentation
Venue: arXiv cs.CR 2025
Year: 2025

Censorship and censorship circumvention are closely connected, and each is constantly making decisions in reaction to the other. When censors deploy a new Internet censorship technique, the anti-censorship community scrambles to find and develop circumvention strategies against the censor's new strategy, i.e., by targeting and exploiting specific vulnerabilities in the new censorship mechanism. We believe that over-reliance on such a reactive approach to circumvention has given the censors the upper hand in the censorship arms race, becoming a key reason for the inefficacy of in-the-wild circumvention systems. Therefore, we argue for a proactive approach to censorship research: the anti-censorship community should be able to proactively develop circumvention mechanisms against hypothetical or futuristic censorship strategies. To facilitate proactive censorship research, we design and implement CensorLab, a generic platform for emulating Internet censorship scenarios. CensorLab aims to complement currently reactive circumvention research by efficiently emulating past, present, and hypothetical censorship strategies in realistic network environments. Specifically, CensorLab aims to (1) support all censorship mechanisms previously or currently deployed by real-world censors; (2) support the emulation of hypothetical (not-yet-deployed) censorship strategies including advanced data-driven censorship mechanisms (e.g., ML-based traffic classifiers); (3) provide an easy-to-use platform for researchers and practitioners enabling them to perform extensive experimentation; and (4) operate efficiently with minimal overhead. We have implemented CensorLab as a fully functional, flexible, and high-performance platform, and showcase how it can be used to emulate a wide range of censorship scenarios, from traditional IP blocking and keyword filtering to hypothetical ML-based censorship mechanisms.
Human-Readable Adversarial Prompts: An Investigation into LLM Vulnerabilities Using Situational Context
Venue: arXiv cs.CL 2025
Year: 2025

As the AI systems become deeply embedded in social media platforms, we've uncovered a concerning security vulnerability that goes beyond traditional adversarial attacks. It becomes important to assess the risks of LLMs before the general public use them on social media platforms to avoid any adverse impacts. Unlike obvious nonsensical text strings that safety systems can easily catch, our work reveals that human-readable situation-driven adversarial full-prompts that leverage situational context are effective but much harder to detect. We found that skilled attackers can exploit the vulnerabilities in open-source and proprietary LLMs to make a malicious user query safe for LLMs, resulting in generating a harmful response. This raises an important question about the vulnerabilities of LLMs. To measure the robustness against human-readable attacks, which now present a potent threat, our research makes three major contributions. First, we developed attacks that use movie scripts as situational contextual frameworks, creating natural-looking full-prompts that trick LLMs into generating harmful content. Second, we developed a method to transform gibberish adversarial text into readable, innocuous content that still exploits vulnerabilities when used within the full-prompts. Finally, we enhanced the AdvPrompter framework with p-nucleus sampling to generate diverse human-readable adversarial texts that significantly improve attack effectiveness against models like GPT-3.5-Turbo-0125 and Gemma-7b. Our findings show that these systems can be manipulated to operate beyond their intended ethical boundaries when presented with seemingly normal prompts that contain hidden adversarial elements. By identifying these vulnerabilities, we aim to drive the development of more robust safety mechanisms that can withstand sophisticated attacks in real-world applications.
LearnLM: Improving Gemini for Learning
Venue: arXiv cs.CY 2025
Year: 2025

Today's generative AI systems are tuned to present information by default, rather than engage users in service of learning as a human tutor would. To address the wide range of potential education use cases for these systems, we reframe the challenge of injecting pedagogical behavior as one of \textit{pedagogical instruction following}, where training and evaluation examples include system-level instructions describing the specific pedagogy attributes present or desired in subsequent model turns. This framing avoids committing our models to any particular definition of pedagogy, and instead allows teachers or developers to specify desired model behavior. It also clears a path to improving Gemini models for learning -- by enabling the addition of our pedagogical data to post-training mixtures -- alongside their rapidly expanding set of capabilities. Both represent important changes from our initial tech report. We show how training with pedagogical instruction following produces a LearnLM model (available on Google AI Studio) that experts substantially prefer across a diverse set of learning scenarios, with average preference strengths of +31\% over GPT-4o, +11\% over Claude 3.5 Sonnet, and +13\% over the Gemini 1.5 Pro model on which LearnLM was based.
MOL-Mamba: Enhancing Molecular Representation with Structural & Electronic Insights
Venue: arXiv cs.LG 2025
Year: 2025

Molecular representation learning plays a crucial role in various downstream tasks, such as molecular property prediction and drug design. To accurately represent molecules, Graph Neural Networks (GNNs) and Graph Transformers (GTs) have shown potential in the realm of self-supervised pretraining. However, existing approaches often overlook the relationship between molecular structure and electronic information, as well as the internal semantic reasoning within molecules. This omission of fundamental chemical knowledge in graph semantics leads to incomplete molecular representations, missing the integration of structural and electronic data. To address these issues, we introduce MOL-Mamba, a framework that enhances molecular representation by combining structural and electronic insights. MOL-Mamba consists of an Atom & Fragment Mamba-Graph (MG) for hierarchical structural reasoning and a Mamba-Transformer (MT) fuser for integrating molecular structure and electronic correlation learning. Additionally, we propose a Structural Distribution Collaborative Training and E-semantic Fusion Training framework to further enhance molecular representation learning. Extensive experiments demonstrate that MOL-Mamba outperforms state-of-the-art baselines across eleven chemical-biological molecular datasets.